Search results
Results from the WOW.Com Content Network
Simulated annealing (SA) is a probabilistic technique for approximating the global optimum of a given function. Specifically, it is a metaheuristic to approximate global optimization in a large search space for an optimization problem. For large numbers of local optima, SA can find the global optimum. [1]
Quantum annealing can be compared to simulated annealing, whose "temperature" parameter plays a similar role to quantum annealing's tunneling field strength.In simulated annealing, the temperature determines the probability of moving to a state of higher "energy" from a single current state.
Visualization of simulated annealing solving a three-dimensional travelling salesman problem instance on 120 points. Simulated annealing is a probabilistic algorithm inspired by annealing, a heat treatment method in metallurgy. It is often used when the search space is discrete (e.g., all tours that visit a given set of cities).
This local-optima problem can be cured by using restarts (repeated local search with different initial conditions), randomization, or more complex schemes based on iterations, like iterated local search, on memory, like reactive search optimization, on memory-less stochastic modifications, like simulated annealing.
Despite the many local maxima in this graph, the global maximum can still be found using simulated annealing. Unfortunately, the applicability of simulated annealing is problem-specific because it relies on finding lucky jumps that improve the position. In such extreme examples, hill climbing will most probably produce a local maximum.
For high volume process annealing, gas fired conveyor furnaces are often used. For large workpieces or high quantity parts, car-bottom furnaces are used so workers can easily move the parts in and out. Once the annealing process is successfully completed, workpieces are sometimes left in the oven so the parts cool in a controllable way.
The Simulated annealing algorithm is designed by looking at how the pure crystals form from a heated gaseous state while the system is cooled slowly. [17] The computing problem is redesigned as a simulated annealing exercise and the solutions are arrived at.
The technique of simulated annealing, by which an existing MSA produced by another method is refined by a series of rearrangements designed to find better regions of alignment space than the one the input alignment already occupies. Like the genetic algorithm method, simulated annealing maximizes an objective function like the sum-of-pairs ...